The goal of this pilot study is to determine whether common polymorphisms in genes involved in folliculogenesis and ovarian steroidogenesis are associated with menstrual cycle characteristics, time to pregnancy or spontaneous abortion. The proposed study takes advantage of existing data and banked biological specimens from the Study of Women Office Workers. In this study, eligible women aged 18 to 40 kept menstrual diaries and collected urine samples during each menstrual cycle for determination of pregnancy. Data on 3774 menstrual cycles and 207 pregnancies are available from 470 women. In the proposed study, DNA from stored, frozen urine samples will be extracted, amplified and genotyped. We chose candidate genes and polymorphisms based on biologically driven hypotheses about reproductive function; these represent only a subset of potentially relevant genes and polymorphisms. We will examine seven genetic variants of the gonadotropin hormones and receptors (follicle stimulating hormone and luteinizing hormone), central hormones in regulating follicular development and ovarian steroidogenesis. In addition, we will examine three common variants of CYP17 and CYP19, central genes in estrogen biosynthesis. To explore the effects of genetic variation on menstrual function, we will examine three outcomes: menstrual cycle length, menstrual bleed length, and menstrual cycle variability. Cycle length and bleed length have been calculated from daily diaries that women kept during follow-up. Menstrual cycle variability will be defined by the standard deviation of a woman's prospective cycle lengths. We will use generalized estimating equations to model the effects of genetic variation on menstrual cycle length and bleed length, accounting for the dependency between cycles within the same woman. To explore the effects of genetic variation on fertility, we will examine two outcomes: time to pregnancy and risk of spontaneous abortion. Pregnancies have previously been ascertained from hCG analysis of urine samples. Time to pregnancy has been determined by counting the number of menstrual cycles up to and including a conception cycle. We will model the effect of genetic variants on fecundity using discrete survival analysis. The risk of spontaneous abortion will be modeled with logistic regression. While rare mutations in genes involved in folliculogenesis and ovarian steroidogenesis are known to result in dysfunctional reproductive phenotypes, there is currently a very limited body of literature exploring the relationship between common polymorphisms and reproductive function. The proposed pilot study is an efficient use of existing data and biological specimens to explore these relationships among a subset of important genes in reproductive function. [unreadable] [unreadable] [unreadable]